Experiments with Data-Intensive NLP on a Computational Grid

Experiments with Data-Intensive NLP on a Computational Grid

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时间:2019-05-27

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1、ExperimentswithData-IntensiveNLPonaComputationalGridBadenHughes,StevenBirdHaejoongLeeEwanKleinDeptofComputerScienceLinguisticDataConsortiumSchoolofInformaticsUniversityofMelbourneUniversityofPennsylvaniaUniversityofEdinburghVictoria3010,AUSTRALIAPhiladelp

2、hiaPA19104,USAEdinburghEH89LW,UKfbadenh,sbg@cs.mu.oz.auhaejoong@ldc.upenn.eduewan@inf.ed.ac.ukAbstractthecommunityneedstoexplorenovelwaystoLargedatabasesofannotatedtextandspeecharewidelycollaborateontheprocessingoflargedatasets.usedfordevelopingandtesting

3、languagetechnologies.How-ever,thesizeofthesecorporaandassociatedlanguagemod-Thispaperreportsaseriesofexperimentselsareoutpacingthegrowthofprocessingpowerandnet-withdata-intensivenaturallanguageprocessingworkbandwidthavailabletomostresearchers.Thesolu-onac

4、omputationalgrid.Acomputationalgridtion,webelieve,istoexploitfourcharacteristicsoflanguagetechnologyresearch:manylargecorporaarealreadyheldfacilitateslarge-scaleanalysisusingdistributedatmostsiteswheretheresearchisconducted;mostdata-resources.Giventheprev

5、alenceoflargedataintensiveprocessingtakesplaceinthedevelopmentphasesourcesinnaturallanguageengineeringandandnotatrun-time;mostprocessingtaskscanbeconstruedasaddinglayersofannotationtoimmutablecorpora;andtheneedforrawcomputationalpowerinthemanyclassesoflan

6、guagemodelscanbeapproximatedastheanalysisandmodellingofsuchdata,thegridsumofsmallermodels.Wereportonaseriesofexperimentscomputingparadigmprovidesefficienciesandwithdata-intensivelanguageprocessingonacomputationalgrid.Keyfeaturesoftheapproachareitsuseofascr

7、iptingscalabilityotherwiseunavailabletonaturallanguageforeasydisseminationofcontrolcodetoprocess-languageengineering.Agridbrokeractsasingnodes,theuseofagridbrokertomanagetheexecutionoftasksonremotenodesandcollatetheiroutput,theuseaatask-farmingengine,empl

8、oyingthesimplestdata-decompositionapproachbywhichparametricandparal-formofdistributioninwhichparalleltasksarelelprocessingofindividuallanguageprocessingcomponentsfullyindependent.Thuswedonotenvisageoccursonsegmented

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